Every active Shopify store we could find, profiled with its category, country, catalog, price level, technologies, age and authority. Search it like a search engine. Export it like a spreadsheet.
There is no official list of Shopify merchants. Anyone who sells to them, competes with them or studies them has to assemble that list somehow.
App store reviews show a few thousand merchants. Search results show the ones with big ad budgets.
The long tail of profitable stores stays invisible.
Stores open and close every day. A list bought last year is already full of dead domains.
Only active stores belong in a prospect list.
A domain alone tells you nothing. You need to know what it sells, where, at what price and with which tools.
That context decides whether a store is worth a call.
Each row in a result table expands into a full profile. These are the fields you can filter, sort and export.
| Field | What it tells you | Filter or sort |
|---|---|---|
| Domain | The store's web address, linked | Search by keyword in domain |
| Store name + description | How the store presents itself | Keyword search |
| Category + subcategory | What the store mainly sells, in an ecommerce taxonomy | Filter |
| Country | Where the store is based | Filter |
| Language | The store's main language | Filter |
| Number of products | Catalog size, a proxy for store maturity | Filter + sort |
| Average product price | Price positioning, from budget to premium | Filter + sort |
| Domain age | How long the domain has existed | Filter + sort |
| Authority | Link strength of the domain | Filter + sort |
| Popularity rank | Where the store sits in global traffic rankings | Filter + sort |
| Technologies | The apps and tools installed, from 4,000+ tracked | Filter by technology |
| Recommended technologies | Tools the store is likely to add next | View on record |
| Contacts | Public contact details for outreach | Export |
| Similar stores | Stores with the closest product offering | View on record |
The data fields reference explains every column, including how to read authority and popularity together.
Different questions start from different places. The explorer supports all of them.
Pick a main category, then drill into a subcategory. Baby Health alone returns more than 250 stores.
The keyword engine searches store names, descriptions and domains together. "Skateboard" returns about 900 stores.
Type any of 4,000+ technologies. Get every store in the database running it.
Around one million stores carry a country. Filter to one and every list becomes a local market view.
Enter a store and get the stores whose product offering is closest to it.
Copy the filters, swap in your own category, and you have a list in under a minute.
Young stores earning links fast usually sell something people talk about.
Surfaces designer crates, luxury beds, pet furniture and modern aquariums.
A ready-made prospect list for a loyalty or rewards app, ranked by fit.
Agencies and freelancers use this to find stores ready for a redesign or ads.
Translation, localization and regional payment providers start here.
The enterprise end of a category, for partnerships and account-based sales.
Six kinds of teams rely on it every week. Each one filters the same million stores in a different way.
Find stores that match your ideal install profile before they find your app.
Fill the pipeline with stores in the verticals you know best.
Every store selling in your category is a potential stockist or shipping client.
Screen whole categories for stores with momentum before anyone lists them for sale.
Know every competitor, not just the ones that show up in ads.
Merchant acquisition teams need volume and fit, fast.
No single number tells the story. These combinations do.
Low age with high authority means momentum. High age with low authority means a store that never took off.
Catalog size and average price together describe the business model.
Traffic rank shows reach. The installed tools show how seriously the store invests.
The pair defines a market. Compare the same category across countries to find white space.
Every major ecommerce category is covered. These are the ones our users search most, and the question they usually ask.
Sort by average price to separate designer labels from fast-fashion resellers.
Filter by subscription tools to find replenishment brands.
High-price pet stores cluster around furniture, travel gear and premium food.
Large catalogs with high prices often need freight and financing partners.
Small catalogs with repeat-purchase tools mark DTC food brands.
Young domains with strong authority point to the next breakout gear brands.
Keyword search finds specialists that category filters alone would miss.
Combine keyword and subcategory to build a clean supplement list.
The Baby Health subcategory alone holds more than 250 stores.
Many small catalogs, ideal for marketplace and wholesale outreach.
Price filters isolate fine jewelry from fashion accessories.
Pair store lists with product trends to time outreach before peak season.
Most store lists fail for the same reasons. Here is how to avoid each one.
A list of every store is not a target list. Narrow by category, size and stack before you export.
A store with ten products and one with a thousand need different pitches. Segment by catalog and price.
If a store already runs the tool you sell, it needs a switch pitch. If it runs nothing in your slot, it needs an add-on pitch.
Generic emails get ignored. Mention the store's category, its tools or a product it sells.
Stores change every month. Re-run your saved filters regularly and work the new rows first.
Your best customers are the best seed. Use similar stores to find more of them.
If you can phrase the question, you can usually answer it with two or three filters.
| Your question | Filters to use | Sort by |
|---|---|---|
| How many stores sell standing desks? | Keyword: standing desk | Popularity |
| Which pet stores in Canada are premium? | Category: pets, country: Canada | Average price |
| Which new fashion stores are taking off? | Category: fashion, age up to 5 years | Authority |
| Who runs a rival reviews app? | Technology: the rival app | Product count |
| Which large stores will add live chat next? | Recommender: live chat, products 500+ | Fit score |
| Which German stores sell home goods? | Language: German, category: home | Product count |
| Who are my ten closest competitors? | Similar stores to your domain | Similarity |
| Which stores could stock my product? | Keyword for your product type | Average price |
| Which small stores need an agency? | Products under 30, age under 2 years | Popularity |
| Where is a category most crowded? | One category, each country in turn | Count of rows |
Write down your ideal store: category, country, size, stack.
Translate it into filters in the explorer.
Open ten records and check the fit by eye.
Download CSV, Excel or PDF.
Load into your CRM or generate AI emails per row.
On both plans, the AI cold email generator writes a first message for any store in your list.
| Question | Manual research | LeadsQuantum |
|---|---|---|
| How many stores can you review? | Dozens per day | 1M+ in one search |
| Do you know their tools? | Only if you inspect each site | 4,000+ technologies per store |
| Can you rank by momentum? | Guesswork | Age, authority and popularity side by side |
| Can you compare price levels? | Click through each catalog | Average price on every row |
| Can you hand it to a rep? | Copy and paste | CSV, Excel, PDF export |
| What does it cost? | Hours of analyst time every week | From $999 per year |
Non-ecommerce sites, classified into 440 categories, for wider campaigns.
More than one million active online stores.
Inactive and closed stores are not part of the active set.
Both plans, from $999 per year, including technology filters.
Store records include public contact information where available, and it comes with your export.
Use product count, average price, popularity rank and authority together. They separate small stores from serious ones reliably.
Yes. Around one million stores carry a country, across all major ecommerce markets.
Use the export button on any result table. Choose CSV, Excel or PDF.
Yes. Keyword search covers store names, descriptions and domains.
Average prices are shown in US dollars, so stores compare across markets.
The demo explorer shows the store table. The guide has screenshots of every view.
Write to [email protected] with your filters and we will quote it.
Store tables export on both plans. Technology and recommender reports have row limits per plan, from 100 up to 10,000 rows.
The store set focuses on active online stores. Sites on other ecommerce platforms are found through the technology lookup across 5 million popular domains.
Review lists show only merchants who left a review, usually a few thousand per app.
The database covers more than a million active stores, reviewers or not.
Advanced includes 2 users and Enterprise 5.
Search, filter, rank and export. Plans start at $999 per year with 10,000 searches a month.